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Factors Influencing Mountain Lion Kill Rates Across Three Ecosystems in the Americas Steven Cross [email protected]

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Factors Influencing Mountain Lion Kill Rates Across Three Ecosystems in the Americas Steven Cross Steven.Cross@Umontana.Edu University of Montana ScholarWorks at University of Montana Undergraduate Theses and Professional Papers 2017 Factors Influencing Mountain Lion Kill Rates Across Three Ecosystems in the Americas steven cross [email protected] Follow this and additional works at: https://scholarworks.umt.edu/utpp Part of the Biology Commons, and the Zoology Commons Let us know how access to this document benefits ou.y Recommended Citation cross, steven, "Factors Influencing Mountain Lion Kill Rates Across Three Ecosystems in the Americas" (2017). Undergraduate Theses and Professional Papers. 178. https://scholarworks.umt.edu/utpp/178 This Thesis is brought to you for free and open access by ScholarWorks at University of Montana. It has been accepted for inclusion in Undergraduate Theses and Professional Papers by an authorized administrator of ScholarWorks at University of Montana. For more information, please contact [email protected]. Factors Influencing Mountain Lion Kill Rates Across Three Ecosystems in the Americas By Steven Cross Undergraduate Thesis Wildlife Biology Program University of Montana Missoula, MT May 2017 Approved by: Dr. Mark Hebblewhite, Chair, Wildlife Biology Program Dr. Hugh Robinson, Committee Member, Wildlife Biology Program Dr. Jedediah Brodie, Committee Member, Wildlife Biology Program Wildlife Biology Program 1 Abstract Kill rate, defined as the number of prey killed per predator per unit time, is a key component to understanding predator-prey dynamics. A multitude of factors may affect kill rates, including, variation in age, sex, weight, or presence of offspring of either predator or prey species (intraspecific variation) and events such as the theft of a kill made by another animal (kleptoparasitism). These factors may influence the time a predator spends locating prey (search time) and the pursuing, killing, and consumption of prey (handling time). The sum of search time and handling time may be measured as the time between a subsequent kill, a metric I will use to make inferences on what affects mountain lion (Puma concolor) kill rates. Utilizing kill data obtained from Global Positioning System (GPS) collared mountain lions of Colorado, Wyoming, and Patagonia, I investigated the impacts of: 1) mountain lion sex, 2) mountain lion age, 3) accompaniment of offspring with mountain lion females, 4) prey weight, and 5) the presence of bears (habitual kleptoparasites) throughout study periods. Applying these factors, I determined the most parsimonious and biologically sound statistical model, best describing sources of variation in time between kills for mountain lions. Determinant factors were: age of a mountain lion, in which a juvenile (<2 years old) was predicted to killed less often than an adult (>2 years old); presence of offspring, in which a female with accompanying offspring was predicted to kill more often than a mountain lion without; per kg of prey weight, in which time between kills was predicted to increase as the weight of a prey item increased; and based on bear activity, in which a mountain lion was predicted to kill more often when bears were active on the landscape than when they were not active. Further knowledge on this subject may be useful for harvest management of mountain lions regarding the lessening of impacts of predation on ungulate populations of concern, through age class and reproductive status targeting. Furthermore, I show some evidence of the indirect impacts of kleptoparasitism on ungulate populations, through the direct impacts on kill rates of predators such as the mountain lion from kleptoparasitic bear species. 2 Introduction Fundamental to understanding the impacts of predators on prey, is in the investigation of the impacts of predation, how predation is compensated by prey, and which individual prey are killed (Mills 2013). Predation itself is the product of the functional response and the numerical response of a predator in relation to a prey population. Put simply, the numerical response is the number of predators given the number of prey, while the functional response (also known as the kill rate), describes the numbers of prey killed per individual predator per unit time (Holling 1959 and Mills 2013). Adequate understanding of predator-prey systems and population dynamics relies in part on the knowledge of factors causing the kill rate of a predator to vary (Knopff et al. 2010). A multitude of factors may be influential to a predator’s kill rate, including energetic requirements, interspecific competition, and intraspecific variation among both a predator and its prey regarding sex and age (Elbroch et al. 2014, Lima and Dill 1990, Miller et al 2014). For enigmatic species, such as the mountain lion (Puma concolor), quantifying kill rates through field efforts in a natural habitat is extremely challenging. However, kill rates have been hypothesized to be driven by metabolic requirements (Ackerman et al. 1986). Thus, energetic models formed from basal metabolic rate and the energetic costs connected to activity, are utilized to estimate energetic requirements and the kill rates required to meet those energetic demands (Elbroch et al. 2014). However, estimated kill rates for the mountain lion rarely align with kill rates quantified through field observations (Elbroch et al. 2014 and Knopff et al. 2010). Many mountain lion studies have quantified empirical kill and consumption rates that were significantly greater than the rate predicted energetically (Elbroch et al. 2014 and Knopff et al. 2010). The discrepancy between predicted and observed kill rates could be the result of prevalent 3 energetic models relying solely on physiological values (e.g. mass and energy budgets) to estimate kill rates (Elbroch et al. 2014). Mountain lions often experience interspecific competition and species interactions with those of the scavenger guild (Elbroch et al. 2015b and Elbroch et al. 2014). Studies suggest that mountain lions seem to abandon and/or increase frequency of kills in the face competition and harassment from scavengers and other predators (Knopff et al. 2010, Elbroch et al. 2015a, Elbroch at el. 2015b, Elbroch et al. 2013b). These forms of species interaction seem to significantly affect mountain lion fitness and foraging behavior (Elbroch et al. 2015a). Mountain lions of the Southern Greater Yellowstone Ecosystem experienced increased starvation, altered prey selection, and increased mortality due to competition from reintroduced gray wolves (Elbroch et al. 2015b). In both Colorado and northern California, mountain lions were found to increase the frequency of kills to compensate for kleptoparasitism by black bears (Ursus americanus) (Elbroch et al. 2015a). Mountain lions of Patagonia suffered kleptoparasitism by scavengers in the form of Andean condors (Vultur gryphus), resulting in reduced handling time at kills, abandonment of kills, and increased frequency of kills (Elbroch et al. 2013b). Similarly, ravens (Corvus corax) were revealed to scavenge up to 75% of the edible biomass of kills made by small grey wolf (Canis lupus) packs (2-3 individuals) in the Yukon, thereby increasing kill rates in those packs (Kaczensky et al. 2005). Mountain lions, being solitary ambush hunters, are seemingly limited in their ability to balance both the consumption and defense of kills from scavengers and kleptoparasites (Elbroch et al. 2015a, Elbroch et al. 2013b, Husseman et al. 2003). Thus, mountain lions may need to increase kill rates to counteract biomass acquisition loss. In contrast, Amur tigers (Panthera tigris altaica), a solitary felid who also ambushes prey, demonstrated higher empirical kill rates than energetically predicted despite little to no 4 interspecific competition (Miller et al. 2014). Amur tigers were thought to be using an optimal foraging strategy, in which tigers were actively minimizing risk of starvation instead of simply meeting daily basal energetic minimums. By increasing their kill rates (thereby increasing overall mean consumption rate (kg/day)), Amur tigers were thought to be reducing their chances of starvation over a given time (essentially killing as often as possible), thus prey encounter rate appeared to be a driving factor in determining kill rates (Miller et al. 2014). This coincides with the notion that solitary felids are random predators that kill prey as available within normal prey size limits (Husseman et al. 2003). Mountain lion kill rates have been shown to be variable per age, sex, and reproductive status though the direction and magnitude of variation is inconsistent among various studies (Knopff et al. 2010 and Pierce et al. 2000). Females with kittens often have higher predation rates compared to solitary mountain lions due to higher energetic requirements as kittens grow (Knopff et al. 2010). Adult males often have higher kill rates than adult females, although Knopff et al. (2010) found that though males in their study had lower kill rates, prey size and consequently, biomass acquisition was higher. This may stem from the larger size of males allowing for the take of larger prey with less risk than smaller females (Sunquist and Sunquist 1989). Subadults usually kill less often and rely more on nonungulate prey items compared to adults, possibly due to lack of experience (Murphy 1998). Risk of injury for subadults during a predation event may lead to choosing prey that can be adequately handled (Pierce et al. 2000). Utilizing kill data obtained from GPS-collared mountain lions of Colorado, Wyoming, and Patagonia,
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